Every week now seems to bring a flurry of think pieces on how artificial intelligence will change everything, including healthcare (and especially radiology). Few break any new developments or insights, but an article posted to The Conversation website Dec. 7 does an uncommonly fine job explaining how much harder it will be for AI to diagnose a disease than, say, recognize a face. 

Researchers at the Cincinnati Children’s Hospital Medical Center used video goggles to distract children undergoing PET or CT scans to determine whether they created CT and PET artifacts. 

This year’s annual RSNA conference featured a session titled, "Hot Topics in Breast Imaging," which outlined a variety of sub-topics including over diagnosis, trends in breast density assessment over time and breast cancer screening.

The RSNA 2016 session, "Next Generation Infrastructure for Medical Imaging," introduced participants to the importance of image sharing and exchange with regard to the quality of care a radiologist delivers.

Online portals where patients can schedule appointments with physicians or contact providers are becoming more common, but many don’t include patient medical images or a way to contact radiologists. If practices are planning on building such a system, they shouldn’t leave patients out of the process.

To better understand the brain, a group of bioengineers from Lehigh University are using space-division multiplexing optical coherence tomography (SDM-OCT) to potentially map the brain. 

The Annual Survey Database from the American Hospital Association (AHA) released data showing the percentage of hospitals with access to an imaging center.

Chris Tomlinson, MBA, CRA, FAHRA, of CHOP and RACH envisions a large pediatric radiology network where everyone is connected and imaging results can easily be shared in seconds.